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Non-iterative, feature-preserving mesh smoothing

Published: 01 July 2003 Publication History

Abstract

With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes.

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  • (2024)Mesh Denoising Using Filtering Coefficients Jointly Aware of Noise and GeometryProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681143(1791-1799)Online publication date: 28-Oct-2024
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cover image ACM Conferences
SIGGRAPH '03: ACM SIGGRAPH 2003 Papers
July 2003
683 pages
ISBN:1581137095
DOI:10.1145/1201775
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 July 2003

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Author Tags

  1. anisotropic diffusion
  2. bilateral filtering
  3. mesh fairing
  4. mesh processing
  5. mesh smoothing
  6. robust estimation

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SIGGRAPH '03 Paper Acceptance Rate 81 of 424 submissions, 19%;
Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Cited By

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  • (2024)Mesh Denoising Using Filtering Coefficients Jointly Aware of Noise and GeometryProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681143(1791-1799)Online publication date: 28-Oct-2024
  • (2024)Hyper-MD: Mesh Denoising with Customized Parameters Aware of Noise Intensity and Geometric Characteristics2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00445(4651-4660)Online publication date: 16-Jun-2024
  • (2024)A noise-reduction algorithm for raw 3D point cloud data of asphalt pavement surface textureScientific Reports10.1038/s41598-024-65233-814:1Online publication date: 18-Jul-2024
  • (2024)Feature-preserving Shrink Wrapping with Adaptive AlphaComputer Aided Geometric Design10.1016/j.cagd.2024.102321(102321)Online publication date: Apr-2024
  • (2023)Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging DataApplied Sciences10.3390/app1314808013:14(8080)Online publication date: 11-Jul-2023
  • (2023)Glass façade segmentation and repair for aerial photogrammetric 3D building models with multiple constraintsInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2023.103242118(103242)Online publication date: Apr-2023
  • (2023)Adaptive and propagated mesh filteringComputer-Aided Design10.1016/j.cad.2022.103422154:COnline publication date: 1-Jan-2023
  • (2022)Reflective Noise Filtering of Large-Scale Point Cloud Using TransformerRemote Sensing10.3390/rs1403057714:3(577)Online publication date: 26-Jan-2022
  • (2022)A Novel Structure Adaptive Algorithm for Feature-preserving 3D Mesh Denoising2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP55362.2022.9949062(1-6)Online publication date: 26-Sep-2022
  • (2022)DMD-Net: Deep Mesh Denoising Network2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956054(3168-3175)Online publication date: 21-Aug-2022
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